New paper on detecting and quantifying subclonal selection in cancer sequencing data by CDB's Marc Williams and Chris Barnes
30 May 2018
Tumour growth is an evolutionary process. Cells grow, replicate and accumulate mutations over time, some of which confer growth advantages or enable them to survive in new conditions. These evolutionary forces can create heterogenous populations of cells, which makes treating many cancers very difficult.
This work, published in Nature Genetics by Marc Williams and Chris Barnes, and in collaboration with Andrea Sottoriva (Institute of Cancer Research) and Trevor Graham (Barts Cancer Institute), describes a new mathematical modelling approach to understand DNA sequence data from human cancer biopsies. Combining simple mathematical models of growing populations and Bayesian statistics, the authors are able to infer how a tumour has evolved and for the first time quantify important aspects of the dynamics including mutation rate and fitness changes. In the long-term, such models could be used to predict a tumour’s future growth within a patient and guide therapeutic strategies.
Read full paper: Quantification of subclonal selection in cancer from bulk sequencing data
Authors: Marc J. Williams, Benjamin Werner, Timon Heide, Christina Curtis, Chris P. Barnes, Andrea Sottoriva & Trevor A. Graham
Read also: Barts Cancer Institute press release
Media coverage: The Times